






'''simple docstring'''





import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_UpperCamelCase						:						List[Any]  =					2


class  snake_case__      :




					def __init__(  self  :		Tuple				, *,  # begin keyword-only arguments
					    _A  :		int="<s>"				, _A  :		Tuple="<pad>"				, _A  :		Any="</s>"				, _A  :		List[Any]="<unk>"				, _A  :		List[str]=None				, )    ->							str:
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[Any]          =       bos, unk, pad, eos
								UpperCAmelCase_      :       Optional[int]          =       []
								UpperCAmelCase_      :       Dict          =       []
								UpperCAmelCase_      :       Any          =       {}
								UpperCAmelCase_      :       Any          =       self.add_symbol(_A      )
								UpperCAmelCase_      :       Optional[int]          =       self.add_symbol(_A      )
								UpperCAmelCase_      :       Any          =       self.add_symbol(_A      )
								UpperCAmelCase_      :       List[str]          =       self.add_symbol(_A      )
								if extra_special_symbols:
											for s in extra_special_symbols:
														self.add_symbol(_A      )
								UpperCAmelCase_      :       Dict          =       len(self.symbols      )




					def __eq__(  self  :		int				, _A  :		List[str]      )    ->							Dict:
								return self.indices == other.indices




					def __getitem__(  self  :		Dict				, _A  :		str      )    ->							Dict:
								if idx < len(self.symbols      ):
											return self.symbols[idx]
								return self.unk_word




					def __len__(  self  :		Dict      )    ->							Dict:
								return len(self.symbols      )




					def __contains__(  self  :		List[str]				, _A  :		Optional[int]      )    ->							List[Any]:
								return sym in self.indices




					@classmethod
					def 		A      (  cls  :		str				, _A  :		Any      )    ->							Dict:
								UpperCAmelCase_      :       str          =       cls()
								d.add_from_file(_A      )
								return d




					def 		A      (  self  :		Tuple				, _A  :		Optional[Any]				, _A  :		Union[str, Any]=1				, _A  :		Optional[Any]=False      )    ->							Union[str, Any]:
								if word in self.indices and not overwrite:
											UpperCAmelCase_      :       Tuple          =       self.indices[word]
											UpperCAmelCase_      :       List[Any]          =       self.count[idx] + n
											return idx
								else:
											UpperCAmelCase_      :       List[Any]          =       len(self.symbols      )
											UpperCAmelCase_      :       Optional[int]          =       idx
											self.symbols.append(_A      )
											self.count.append(_A      )
											return idx




					def 		A      (  self  :		Any				, _A  :		Optional[Any]      )    ->							Any:
								return 0





					def 		A      (  self  :		Any				, _A  :		int      )    ->							Union[str, Any]:
								if isinstance(_A				, _A      ):
											try:
														with open(_A				, '''r'''				, encoding='''utf-8'''      ) as fd:
																	self.add_from_file(_A      )
											except FileNotFoundError as fnfe:
														raise fnfe
											except UnicodeError:
														raise Exception('''Incorrect encoding detected in {}, please rebuild the dataset'''.format(_A      )      )
											return

								UpperCAmelCase_      :       Dict          =       f.readlines()
								UpperCAmelCase_      :       Dict          =       self._load_meta(_A      )

								for line in lines[indices_start_line:]:
											try:
														UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       line.rstrip().rsplit(''' '''				, 1      )
														if field == "#fairseq:overwrite":
																	UpperCAmelCase_      :       Union[str, Any]          =       True
																	UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       line.rsplit(''' '''				, 1      )
														else:
																	UpperCAmelCase_      :       Optional[Any]          =       False
														UpperCAmelCase_      :       Optional[int]          =       int(_A      )
														UpperCAmelCase_      :       Tuple          =       line
														if word in self and not overwrite:
																	raise RuntimeError(
																	    '''Duplicate word found when loading Dictionary: \'{}\'. '''
																	    '''Duplicate words can overwrite earlier ones by adding the '''
																	    '''#fairseq:overwrite flag at the end of the corresponding row '''
																	    '''in the dictionary file. If using the Camembert model, please '''
																	    '''download an updated copy of the model file.'''.format(_A      )      )
														self.add_symbol(_A				, n=_A				, overwrite=_A      )
											except ValueError:
														raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt> [flags]\''''      )






def __UpperCAmelCase   (					A					:			int		)    ->   Dict:
			# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
			# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
			UpperCAmelCase_      :       List[str]          =       dict((re.sub(r'''@@$'''			,       ''''''			,       A		), v) if k.endswith('''@@'''		) else (re.sub(r'''$'''			,       '''</w>'''			,       A		), v) for k, v in d.items()		)
			UpperCAmelCase_      :       Dict          =       '''<s> <pad> </s> <unk>'''.split()
			# restore the special tokens
			for k in keep_keys:
						del da[F"{k}</w>"]
						UpperCAmelCase_      :       Union[str, Any]          =       d[k]  # restore
			return da
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Optional[int]		)    ->   int:
			# prep
			if not os.path.exists(A		):
						raise ValueError(F"path {biogpt_checkpoint_path} does not exist!"		)
			os.makedirs(A			,       exist_ok=A		)
			print(F"Writing results to {pytorch_dump_folder_path}"		)

			# handle various types of models

			UpperCAmelCase_      :       str          =       os.path.join(A			,       '''checkpoint.pt'''		)
			if not os.path.isfile(A		):
						raise ValueError(F"path to the file {checkpoint_file} does not exist!"		)
			UpperCAmelCase_      :       Dict          =       torch.load(A			,       map_location='''cpu'''		)

			UpperCAmelCase_      :       Any          =       chkpt['''cfg''']['''model''']

			# dicts
			UpperCAmelCase_      :       List[str]          =       os.path.join(A			,       '''dict.txt'''		)
			if not os.path.isfile(A		):
						raise ValueError(F"path to the file {dict_file} does not exist!"		)
			UpperCAmelCase_      :       Dict          =       Dictionary.load(A		)
			UpperCAmelCase_      :       Optional[Any]          =       rewrite_dict_keys(src_dict.indices		)
			UpperCAmelCase_      :       Optional[int]          =       len(A		)
			UpperCAmelCase_      :       Dict          =       os.path.join(A			,       VOCAB_FILES_NAMES['''vocab_file''']		)
			print(F"Generating {src_vocab_file} of {src_vocab_size} records"		)
			with open(A			,       '''w'''			,       encoding='''utf-8'''		) as f:
						f.write(json.dumps(A			,       ensure_ascii=A			,       indent=A		)		)

			# merges_file (bpecodes)
			UpperCAmelCase_      :       Any          =       os.path.join(A			,       '''bpecodes'''		)
			if not os.path.isfile(A		):
						raise ValueError(F"path to the file {bpecodes_file} does not exist!"		)

			UpperCAmelCase_      :       Any          =       os.path.join(A			,       VOCAB_FILES_NAMES['''merges_file''']		)
			shutil.copyfile(A			,       A		)

			# model config
			UpperCAmelCase_      :       Optional[Any]          =       os.path.join(A			,       '''config.json'''		)

			UpperCAmelCase_      :       Optional[Any]          =       {
			    '''activation_dropout''': args['''activation_dropout'''],
			    '''architectures''': ['''BioGptForCausalLM'''],
			    '''attention_probs_dropout_prob''': args['''attention_dropout'''],
			    '''bos_token_id''': 0,
			    '''eos_token_id''': 2,
			    '''hidden_act''': args['''activation_fn'''],
			    '''hidden_dropout_prob''': args['''dropout'''],
			    '''hidden_size''': args['''decoder_embed_dim'''],
			    '''initializer_range''': 0.02,
			    '''intermediate_size''': args['''decoder_ffn_embed_dim'''],
			    '''layer_norm_eps''': 1e-12,
			    '''layerdrop''': args['''decoder_layerdrop'''],
			    '''max_position_embeddings''': args['''max_target_positions'''],
			    '''model_type''': '''biogpt''',
			    '''num_attention_heads''': args['''decoder_attention_heads'''],
			    '''num_hidden_layers''': args['''decoder_layers'''],
			    '''pad_token_id''': 1,
			    '''scale_embedding''': not args['''no_scale_embedding'''],
			    '''tie_word_embeddings''': args['''share_decoder_input_output_embed'''],
			    '''vocab_size''': src_vocab_size,
			}

			# good hparam defaults to start with

			print(F"Generating {biogpt_model_config_file}"		)
			with open(A			,       '''w'''			,       encoding='''utf-8'''		) as f:
						f.write(json.dumps(A			,       ensure_ascii=A			,       indent=A		)		)

			# tokenizer config
			UpperCAmelCase_      :       Any          =       os.path.join(A			,       A		)

			UpperCAmelCase_      :       Optional[Any]          =       {
			    '''bos_token''': '''<s>''',
			    '''eos_token''': '''</s>''',
			    '''model_max_length''': 1_0_2_4,
			    '''pad_token''': '''<pad>''',
			    '''special_tokens_map_file''': None,
			    '''tokenizer_class''': '''BioGptTokenizer''',
			    '''unk_token''': '''<unk>''',
			}

			print(F"Generating {biogpt_tokenizer_config_file}"		)
			with open(A			,       '''w'''			,       encoding='''utf-8'''		) as f:
						f.write(json.dumps(A			,       ensure_ascii=A			,       indent=A		)		)

			# model
			UpperCAmelCase_      :       Optional[int]          =       chkpt['''model''']

			# remove unneeded keys
			UpperCAmelCase_      :       Dict          =       [
			    '''decoder.version''',
			]
			for k in ignore_keys:
						model_state_dict.pop(A			,       A		)

			UpperCAmelCase_      :       List[str]          =       list(model_state_dict.keys()		)
			for layer_name in layer_names:
						if layer_name.endswith('''output_projection.weight'''		):
									UpperCAmelCase_      :       str          =       model_state_dict.pop(A		)
						else:
									UpperCAmelCase_      :       Union[str, Any]          =       model_state_dict.pop(A		)

			UpperCAmelCase_      :       str          =       BioGptConfig.from_pretrained(A		)
			UpperCAmelCase_      :       Optional[int]          =       BioGptForCausalLM(A		)

			# check that it loads ok
			model_new.load_state_dict(A		)

			# save
			UpperCAmelCase_      :       Union[str, Any]          =       os.path.join(A			,       A		)
			print(F"Generating {pytorch_weights_dump_path}"		)
			torch.save(A			,       A		)

			print('''Conversion is done!'''		)


if __name__ == "__main__":
						_UpperCamelCase						:						Tuple  =					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--biogpt_checkpoint_path',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
						        ' bpecodes, etc.'
						    ),
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						_UpperCamelCase						:						Any  =					parser.parse_args()
						convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''





import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


_UpperCamelCase						:						List[str]  =					re.compile(R'\b(a|an|the)\b', re.UNICODE)

_UpperCamelCase						:						Optional[int]  =					None
def __UpperCAmelCase   (					)    ->   Optional[Any]:
			UpperCAmelCase_      :       List[Any]          =       argparse.ArgumentParser('''Official evaluation script for SQuAD version 2.0.'''		)
			parser.add_argument('''data_file'''			,       metavar='''data.json'''			,       help='''Input data JSON file.'''		)
			parser.add_argument('''pred_file'''			,       metavar='''pred.json'''			,       help='''Model predictions.'''		)
			parser.add_argument(
			    '''--out-file'''			,       '''-o'''			,       metavar='''eval.json'''			,       help='''Write accuracy metrics to file (default is stdout).'''		)
			parser.add_argument(
			    '''--na-prob-file'''			,       '''-n'''			,       metavar='''na_prob.json'''			,       help='''Model estimates of probability of no answer.'''		)
			parser.add_argument(
			    '''--na-prob-thresh'''			,       '''-t'''			,       type=A			,       default=1.0			,       help='''Predict "" if no-answer probability exceeds this (default = 1.0).'''			,       )
			parser.add_argument(
			    '''--out-image-dir'''			,       '''-p'''			,       metavar='''out_images'''			,       default=A			,       help='''Save precision-recall curves to directory.'''		)
			parser.add_argument('''--verbose'''			,       '''-v'''			,       action='''store_true'''		)
			if len(sys.argv		) == 1:
						parser.print_help()
						sys.exit(1		)
			return parser.parse_args()
def __UpperCAmelCase   (					A					:			int		)    ->   Optional[Any]:
			UpperCAmelCase_      :       Optional[Any]          =       {}
			for article in dataset:
						for p in article["paragraphs"]:
									for qa in p["qas"]:
												UpperCAmelCase_      :       Union[str, Any]          =       bool(qa['''answers''']['''text''']		)
			return qid_to_has_ans
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Dict:

			def remove_articles(A					:			Dict		):
						return ARTICLES_REGEX.sub(''' '''			,       A		)

			def white_space_fix(A					:			str		):
						return " ".join(text.split()		)

			def remove_punc(A					:			Tuple		):
						UpperCAmelCase_      :       Dict          =       set(string.punctuation		)
						return "".join(ch for ch in text if ch not in exclude		)

			def lower(A					:			Any		):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(A		)		)		)		)
def __UpperCAmelCase   (					A					:			Optional[Any]		)    ->   Tuple:
			if not s:
						return []
			return normalize_answer(A		).split()
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Dict		)    ->   List[str]:
			return int(normalize_answer(A		) == normalize_answer(A		)		)
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			List[Any]		)    ->   str:
			UpperCAmelCase_      :       Tuple          =       get_tokens(A		)
			UpperCAmelCase_      :       Union[str, Any]          =       get_tokens(A		)
			UpperCAmelCase_      :       Dict          =       collections.Counter(A		) & collections.Counter(A		)
			UpperCAmelCase_      :       Optional[int]          =       sum(common.values()		)
			if len(A		) == 0 or len(A		) == 0:
						# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
						return int(gold_toks == pred_toks		)
			if num_same == 0:
						return 0
			UpperCAmelCase_      :       int          =       1.0 * num_same / len(A		)
			UpperCAmelCase_      :       Any          =       1.0 * num_same / len(A		)
			UpperCAmelCase_      :       Any          =       (2 * precision * recall) / (precision + recall)
			return fa
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			str		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       List[Any]          =       {}
			UpperCAmelCase_      :       str          =       {}
			for article in dataset:
						for p in article["paragraphs"]:
									for qa in p["qas"]:
												UpperCAmelCase_      :       Optional[Any]          =       qa['''id''']
												UpperCAmelCase_      :       List[str]          =       [t for t in qa['''answers''']['''text'''] if normalize_answer(A		)]
												if not gold_answers:
															# For unanswerable questions, only correct answer is empty string
															UpperCAmelCase_      :       Tuple          =       ['''''']
												if qid not in preds:
															print(F"Missing prediction for {qid}"		)
															continue
												UpperCAmelCase_      :       Tuple          =       preds[qid]
												# Take max over all gold answers
												UpperCAmelCase_      :       List[Any]          =       max(compute_exact(A			,       A		) for a in gold_answers		)
												UpperCAmelCase_      :       int          =       max(compute_fa(A			,       A		) for a in gold_answers		)
			return exact_scores, fa_scores
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Tuple			,       A					:			Optional[Any]			,       A					:			List[Any]		)    ->   Optional[int]:
			UpperCAmelCase_      :       Tuple          =       {}
			for qid, s in scores.items():
						UpperCAmelCase_      :       Union[str, Any]          =       na_probs[qid] > na_prob_thresh
						if pred_na:
									UpperCAmelCase_      :       List[str]          =       float(not qid_to_has_ans[qid]		)
						else:
									UpperCAmelCase_      :       Any          =       s
			return new_scores
def __UpperCAmelCase   (					A					:			Any			,       A					:			Any			,       A					:			str=None		)    ->   int:
			if not qid_list:
						UpperCAmelCase_      :       str          =       len(A		)
						return collections.OrderedDict(
						    [
						        ('''exact''', 100.0 * sum(exact_scores.values()		) / total),
						        ('''f1''', 100.0 * sum(fa_scores.values()		) / total),
						        ('''total''', total),
						    ]		)
			else:
						UpperCAmelCase_      :       Dict          =       len(A		)
						return collections.OrderedDict(
						    [
						        ('''exact''', 100.0 * sum(exact_scores[k] for k in qid_list		) / total),
						        ('''f1''', 100.0 * sum(fa_scores[k] for k in qid_list		) / total),
						        ('''total''', total),
						    ]		)
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			Dict			,       A					:			Union[str, Any]		)    ->   Dict:
			for k in new_eval:
						UpperCAmelCase_      :       str          =       new_eval[k]
def __UpperCAmelCase   (					A					:			str			,       A					:			Tuple			,       A					:			Any			,       A					:			List[str]		)    ->   Optional[int]:
			plt.step(A			,       A			,       color='''b'''			,       alpha=0.2			,       where='''post'''		)
			plt.fill_between(A			,       A			,       step='''post'''			,       alpha=0.2			,       color='''b'''		)
			plt.xlabel('''Recall'''		)
			plt.ylabel('''Precision'''		)
			plt.xlim([0.0, 1.05]		)
			plt.ylim([0.0, 1.05]		)
			plt.title(A		)
			plt.savefig(A		)
			plt.clf()
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			Optional[Any]			,       A					:			Optional[int]			,       A					:			Optional[int]			,       A					:			str=None			,       A					:			Any=None		)    ->   Any:
			UpperCAmelCase_      :       Optional[Any]          =       sorted(A			,       key=lambda A		: na_probs[k]		)
			UpperCAmelCase_      :       int          =       0.0
			UpperCAmelCase_      :       List[Any]          =       1.0
			UpperCAmelCase_      :       Optional[int]          =       0.0
			UpperCAmelCase_      :       Optional[Any]          =       [1.0]
			UpperCAmelCase_      :       List[Any]          =       [0.0]
			UpperCAmelCase_      :       Dict          =       0.0
			for i, qid in enumerate(A		):
						if qid_to_has_ans[qid]:
									true_pos += scores[qid]
						UpperCAmelCase_      :       List[Any]          =       true_pos / float(i + 1		)
						UpperCAmelCase_      :       Union[str, Any]          =       true_pos / float(A		)
						if i == len(A		) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
									# i.e., if we can put a threshold after this point
									avg_prec += cur_p * (cur_r - recalls[-1])
									precisions.append(A		)
									recalls.append(A		)
			if out_image:
						plot_pr_curve(A			,       A			,       A			,       A		)
			return {"ap": 100.0 * avg_prec}
def __UpperCAmelCase   (					A					:			List[Any]			,       A					:			str			,       A					:			Any			,       A					:			Optional[int]			,       A					:			Any			,       A					:			List[str]		)    ->   List[Any]:
			if out_image_dir and not os.path.exists(A		):
						os.makedirs(A		)
			UpperCAmelCase_      :       Any          =       sum(1 for v in qid_to_has_ans.values() if v		)
			if num_true_pos == 0:
						return
			UpperCAmelCase_      :       Union[str, Any]          =       make_precision_recall_eval(
			    A			,       A			,       A			,       A			,       out_image=os.path.join(A			,       '''pr_exact.png'''		)			,       title='''Precision-Recall curve for Exact Match score'''			,       )
			UpperCAmelCase_      :       List[str]          =       make_precision_recall_eval(
			    A			,       A			,       A			,       A			,       out_image=os.path.join(A			,       '''pr_f1.png'''		)			,       title='''Precision-Recall curve for F1 score'''			,       )
			UpperCAmelCase_      :       Dict          =       {k: float(A		) for k, v in qid_to_has_ans.items()}
			UpperCAmelCase_      :       Optional[int]          =       make_precision_recall_eval(
			    A			,       A			,       A			,       A			,       out_image=os.path.join(A			,       '''pr_oracle.png'''		)			,       title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'''			,       )
			merge_eval(A			,       A			,       '''pr_exact'''		)
			merge_eval(A			,       A			,       '''pr_f1'''		)
			merge_eval(A			,       A			,       '''pr_oracle'''		)
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			Tuple			,       A					:			str			,       A					:			List[Any]		)    ->   Dict:
			if not qid_list:
						return
			UpperCAmelCase_      :       List[Any]          =       [na_probs[k] for k in qid_list]
			UpperCAmelCase_      :       Union[str, Any]          =       np.ones_like(A		) / float(len(A		)		)
			plt.hist(A			,       weights=A			,       bins=2_0			,       range=(0.0, 1.0)		)
			plt.xlabel('''Model probability of no-answer'''		)
			plt.ylabel('''Proportion of dataset'''		)
			plt.title(F"Histogram of no-answer probability: {name}"		)
			plt.savefig(os.path.join(A			,       F"na_prob_hist_{name}.png"		)		)
			plt.clf()
def __UpperCAmelCase   (					A					:			str			,       A					:			Dict			,       A					:			List[str]			,       A					:			int		)    ->   Optional[Any]:
			UpperCAmelCase_      :       Tuple          =       sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]		)
			UpperCAmelCase_      :       Optional[Any]          =       num_no_ans
			UpperCAmelCase_      :       int          =       cur_score
			UpperCAmelCase_      :       List[Any]          =       0.0
			UpperCAmelCase_      :       Dict          =       sorted(A			,       key=lambda A		: na_probs[k]		)
			for i, qid in enumerate(A		):
						if qid not in scores:
									continue
						if qid_to_has_ans[qid]:
									UpperCAmelCase_      :       Tuple          =       scores[qid]
						else:
									if preds[qid]:
												UpperCAmelCase_      :       Optional[int]          =       -1
									else:
												UpperCAmelCase_      :       int          =       0
						cur_score += diff
						if cur_score > best_score:
									UpperCAmelCase_      :       Dict          =       cur_score
									UpperCAmelCase_      :       Dict          =       na_probs[qid]
			return 100.0 * best_score / len(A		), best_thresh
def __UpperCAmelCase   (					A					:			int			,       A					:			List[Any]			,       A					:			Union[str, Any]			,       A					:			Tuple			,       A					:			str			,       A					:			Union[str, Any]		)    ->   Tuple:
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       find_best_thresh(A			,       A			,       A			,       A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       find_best_thresh(A			,       A			,       A			,       A		)
			UpperCAmelCase_      :       List[str]          =       best_exact
			UpperCAmelCase_      :       List[Any]          =       exact_thresh
			UpperCAmelCase_      :       Any          =       best_fa
			UpperCAmelCase_      :       str          =       fa_thresh
def __UpperCAmelCase   (					)    ->   Tuple:
			with open(OPTS.data_file		) as f:
						UpperCAmelCase_      :       Optional[int]          =       json.load(A		)
						UpperCAmelCase_      :       str          =       dataset_json['''data''']
			with open(OPTS.pred_file		) as f:
						UpperCAmelCase_      :       List[Any]          =       json.load(A		)
			if OPTS.na_prob_file:
						with open(OPTS.na_prob_file		) as f:
									UpperCAmelCase_      :       List[str]          =       json.load(A		)
			else:
						UpperCAmelCase_      :       List[str]          =       {k: 0.0 for k in preds}
			UpperCAmelCase_      :       Optional[Any]          =       make_qid_to_has_ans(A		)  # maps qid to True/False
			UpperCAmelCase_      :       Optional[int]          =       [k for k, v in qid_to_has_ans.items() if v]
			UpperCAmelCase_      :       Dict          =       [k for k, v in qid_to_has_ans.items() if not v]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       get_raw_scores(A			,       A		)
			UpperCAmelCase_      :       Optional[int]          =       apply_no_ans_threshold(A			,       A			,       A			,       OPTS.na_prob_thresh		)
			UpperCAmelCase_      :       Tuple          =       apply_no_ans_threshold(A			,       A			,       A			,       OPTS.na_prob_thresh		)
			UpperCAmelCase_      :       int          =       make_eval_dict(A			,       A		)
			if has_ans_qids:
						UpperCAmelCase_      :       Optional[int]          =       make_eval_dict(A			,       A			,       qid_list=A		)
						merge_eval(A			,       A			,       '''HasAns'''		)
			if no_ans_qids:
						UpperCAmelCase_      :       Dict          =       make_eval_dict(A			,       A			,       qid_list=A		)
						merge_eval(A			,       A			,       '''NoAns'''		)
			if OPTS.na_prob_file:
						find_all_best_thresh(A			,       A			,       A			,       A			,       A			,       A		)
			if OPTS.na_prob_file and OPTS.out_image_dir:
						run_precision_recall_analysis(A			,       A			,       A			,       A			,       A			,       OPTS.out_image_dir		)
						histogram_na_prob(A			,       A			,       OPTS.out_image_dir			,       '''hasAns'''		)
						histogram_na_prob(A			,       A			,       OPTS.out_image_dir			,       '''noAns'''		)
			if OPTS.out_file:
						with open(OPTS.out_file			,       '''w'''		) as f:
									json.dump(A			,       A		)
			else:
						print(json.dumps(A			,       indent=2		)		)


if __name__ == "__main__":
						_UpperCamelCase						:						Union[str, Any]  =					parse_args()
						if OPTS.out_image_dir:
												import matplotlib

												matplotlib.use('Agg')
												import matplotlib.pyplot as plt
						main()


